--- title: "OpenRLHF vs CodeRL" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/openrlhf-openrlhf-vs-salesforce-coderl" tools: ["openrlhf-openrlhf", "salesforce-coderl"] --- # OpenRLHF vs CodeRL *GraphCanon updated Oct 7, 2026* ## Verdict Pick OpenRLHF if openRLHF is a scalable and high-performance reinforcement learning framework from human feedback, built on Ray for distributed scheduling and vLLM for efficient inference, supporting models up to 70B+ parameters; pick CodeRL if codeRL is an advanced tool that uses pretrained models and deep reinforcement learning to generate code. [OpenRLHF](https://openrlhf.readthedocs.io/) reports 10k GitHub stars, 1.0k forks, and 380 open issues, last pushed Sep 17, 2026. [CodeRL](https://github.com/salesforce/CodeRL) has 576 stars, 69 forks, and 42 open issues, last pushed Jun 2, 2026. Figures are from public GitHub metadata via [OpenRLHF's repository](https://github.com/OpenRLHF/OpenRLHF) and [CodeRL's repository](https://github.com/salesforce/CodeRL). | | [OpenRLHF](/tools/openrlhf-openrlhf.md) | [CodeRL](/tools/salesforce-coderl.md) | | --- | --- | --- | | Tagline | Scalable and High-performance Agentic RL Framework based on Ray | CodeRL: Combines pretrained models and reinforcement learning for code generation. | | Stars | 10,014 | 576 | | Forks | 1,019 | 69 | | Open issues | 380 | 42 | | Language | Python | Python | | Adopt for | OpenRLHF is a scalable and high-performance reinforcement learning framework from human feedback, built on Ray for distributed scheduling and vLLM for efficient inference, supporting models up to 70B+ parameters. | CodeRL is an advanced tool that uses pretrained models and deep reinforcement learning to generate code. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | BSD-3-Clause | | Categories | Inference & Serving, Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [OpenRLHF](/tools/openrlhf-openrlhf.md) | [CodeRL](/tools/salesforce-coderl.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 1d | 126d | | Open issues (now) | 380 | 42 | | Stars delta | +123 (30d) | +1 (30d) | | Open issues delta | +13 (30d) | 0 (30d) | | Full report | [trust report](/tools/openrlhf-openrlhf/trust.md) | [trust report](/tools/salesforce-coderl/trust.md) | ## Shared compatibility - **Python**: [OpenRLHF](/tools/openrlhf-openrlhf.md) - Python runtime; [CodeRL](/tools/salesforce-coderl.md) - Python runtime ## Decision facts: OpenRLHF - **Adopt for:** OpenRLHF is a scalable and high-performance reinforcement learning framework from human feedback, built on Ray for distributed scheduling and vLLM for efficient inference, supporting models up to 70B+ parameters. ## Decision facts: CodeRL - **Adopt for:** CodeRL is an advanced tool that uses pretrained models and deep reinforcement learning to generate code. ## Choose when ### Choose OpenRLHF if… - License: OpenRLHF is Apache-2.0, CodeRL is BSD-3-Clause. - Tags unique to OpenRLHF: large-language-models, proximal-policy-optimization, raylib, reinforcement learning. - Also covers Inference & Serving. - When you need to train large models up to 70B+ parameters with efficient distributed scheduling and high-performance inference. ### Choose CodeRL if… - License: CodeRL is BSD-3-Clause, OpenRLHF is Apache-2.0. - Tags unique to CodeRL: ai, codegeneration, languagemodel, machinelearning. - Also covers Developer Tools. - When you need to generate complex and context-aware code snippets utilizing the latest in reinforcement learning techniques. ## When NOT to use OpenRLHF - If your project does not require support for large models or distributed training, as OpenRLHF's complexity and resource requirements may be unnecessary. - When you prefer a framework that does not rely on Ray for distributed scheduling, as OpenRLHF's architecture is tightly coupled with Ray for its distributed capabilities. ## When NOT to use CodeRL - Avoid if your project requires only simple, quick code generation without deep reinforcement learning support. - Do not use if compatibility with versions of the Hugging Face transformers library other than 4.16.1 is critical to avoid potential issues. ## Common questions ### What is the difference between OpenRLHF and CodeRL? OpenRLHF: Scalable and High-performance Agentic RL Framework based on Ray. CodeRL: CodeRL: Combines pretrained models and reinforcement learning for code generation.. See the comparison table for live GitHub stats and shared categories. ### When should I choose OpenRLHF over CodeRL? Choose OpenRLHF over CodeRL when License: OpenRLHF is Apache-2.0, CodeRL is BSD-3-Clause; Tags unique to OpenRLHF: large-language-models, proximal-policy-optimization, raylib, reinforcement learning; Also covers Inference & Serving; When you need to train large models up to 70B+ parameters with efficient distributed scheduling and high-performance inference. ### When should I choose CodeRL over OpenRLHF? Choose CodeRL over OpenRLHF when License: CodeRL is BSD-3-Clause, OpenRLHF is Apache-2.0; Tags unique to CodeRL: ai, codegeneration, languagemodel, machinelearning; Also covers Developer Tools; When you need to generate complex and context-aware code snippets utilizing the latest in reinforcement learning techniques. ### When should I avoid OpenRLHF? If your project does not require support for large models or distributed training, as OpenRLHF's complexity and resource requirements may be unnecessary. When you prefer a framework that does not rely on Ray for distributed scheduling, as OpenRLHF's architecture is tightly coupled with Ray for its distributed capabilities. ### When should I avoid CodeRL? Avoid if your project requires only simple, quick code generation without deep reinforcement learning support. Do not use if compatibility with versions of the Hugging Face transformers library other than 4.16.1 is critical to avoid potential issues. ### Is OpenRLHF or CodeRL more popular on GitHub? OpenRLHF has more GitHub stars (10,014 vs 576). Stars measure visibility, not whether either tool fits your constraints. ### Are OpenRLHF and CodeRL open source? Yes - both are open-source projects on GitHub (OpenRLHF: Apache-2.0, CodeRL: BSD-3-Clause). ### Where can I find alternatives to OpenRLHF or CodeRL? GraphCanon lists graph-backed alternatives at [OpenRLHF alternatives](/tools/openrlhf-openrlhf/alternatives) and [CodeRL alternatives](/tools/salesforce-coderl/alternatives) ([OpenRLHF markdown twin](/tools/openrlhf-openrlhf/alternatives.md), [CodeRL markdown twin](/tools/salesforce-coderl/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/openrlhf-openrlhf-vs-salesforce-coderl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, OpenRLHF or CodeRL? OpenRLHF: Very active. CodeRL: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for OpenRLHF and CodeRL? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [OpenRLHF trust report](/tools/openrlhf-openrlhf/trust); [CodeRL trust report](/tools/salesforce-coderl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=openrlhf-openrlhf`](/api/graphcanon/graph?tool=openrlhf-openrlhf) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_